火力与指挥控制2025，Vol.50Issue(4)：14-19,6.DOI:10.3969/j.issn.1002-0640.2025.04.002
基于改进鸡群优化的一维到达角定位算法
One-dimensional Angle of Arrival Localization Algorithm Based on Improved Chicken Swarm Optimization
摘要
Abstract
Addressing the issues of high computational complexity and poor positioning accuracy in one-dimensional angle of arrival(1-D AOA)localization,this paper proposes a 1-D AOA localization algorithm based on the improved chicken swarm optimization(ICSO)algorithm.The algorithm derives fitness functions based on the measurement models for one-dimensional angle of arrival localization using maximum likelihood estimation and weighted least squares.To enhance convergence speed and mitigate the risk of local optima,improvements are made to the chicken swarm optimization algorithm.Subsequently,the ICSO algorithm is applied to solve the fitness functions for localization.Simulation results indicate that,compared to existing methods,this localization algorithm not only reduces computational complexity but also improves positioning accuracy.The obtained localization results approach the cramer-rao lower Bound(CRLB)and significantly reduce the dependency on the number of observation stations.关键词
无源定位/改进鸡群算法/一维到达角/克拉美罗下界Key words
passive localization/ICSO/one-dimensional angle of arrival/cramer-rao lower bound分类
计算机与自动化引用本文复制引用
郑晓园,曹振乾,晏行伟,张敏,李润泽..基于改进鸡群优化的一维到达角定位算法[J].火力与指挥控制,2025,50(4):14-19,6.基金项目
国家自然科学基金 （）
国家重点实验室基金 （）
河北省科技计划基金资助项目 （）
